1. Pairwise Sequence Alignment Part 2

2. Outline Summary Local and Global alignments FASTA and BLAST algorithms Evaluating significance of alignments Alignment of protein sequences

3. Best score for aligning part of sequences Dynamic programming Algorithm: Smith-Waterman Table cells never score below zero Best score for aligning the full length sequences Dynamic programming Algorithm: Needelman- Wunch Table cells are allowed any score Global Local Pairwise Alignment Summary

4. Gap Scores Example showed -1 score per indel –So gap cost is proportional to its length Biologically, indels occur in groups –We want our gap score to reflect this Standard solution: affine gap model –Once-off cost for opening a gap –Lower cost for extending the gap –Changes required to algorithm

5. Assessing Alignment Significance Compare alignment score to all random alignment scores Compute the mean and the standard deviation (SD) for random scores Compute the deviation (in sd) of the actual score from the mean of random scores Z=(x-mean)/sd Evaluate the significance of the alignment Generate random alignments and calculate their scores

6. Complexity Complexity is determined by size of table –Aligning a sequence of length m against one of length n requires calculating (m  n) cells Estimate: we calculate 10 8 cells per second –Aligning two mRNA sequences of 8,000 bp requires 64,000,000 cells  0.64 seconds –Aligning an mRNA and a 10 7 bp chromosome requires ~10 11 cells  1,000 secs = 15 minutes

7. Complexity for GenBank GenBank contains 3  10 10 base pairs –Searching an mRNA against GenBank requires ~2.5  10 14 cells  2.5  10 6 secs = 1 month! –So each computer could support just one GenBank search per month We need to cut down on alignment –Use a heuristic method to narrow down the part of GenBank that could be of interest

8. Using the pairwise comparison, each database search normally yields 2 groups of scores: genuinely related and unrelated sequences, with some overlap between them. A good search method should completely separate between the 2 score groups. Database Searches

9. Ideal No Good Random Related

10. Heuristic Methods: FASTA and BLAST FASTA (Lipman & Pearson 1985) –First fast sequence searching algorithm for comparing a query sequence against a database BLAST - Basic Local Alignment Search Technique (Altschul et al 1990) –improvement of FASTA: Search speed, ease of use, statistical rigor –Gapped BLAST (Altschul et al 1997)

11. FASTA and BLAST Common idea - a good alignment contains subsequences of absolute identity: –First, identify very short (almost) exact matches. –Next, the best short hits from the 1st step are extended to longer regions of similarity. –Finally, the best hits are optimized using the Smith- Waterman algorithm.

12. FastA locates regions of the query sequence and the search set sequence that have high densities of exact word matches. For DNA sequences the word length used is 6. seq1 seq2 FASTA

13. The 10 highest-scoring sequence regions are saved and re-scored using a scoring matrix. seq1 seq2

14. FastA determines if any of the initial regions from different diagonals may be joined together to form an approximate alignment with gaps. Only non-overlapping regions may be joined. seq1 seq2

15. The score for the joined regions is the sum of the scores of the initial regions minus a joining penalty for each gap. seq1 seq2

16. FastA uses dynamic programming (Smith- Waterman algorithm ) over a narrow band of high scoring diagonals between the query sequence and the search set sequence, to produce an alignment with a new score. seq1 seq2

17. Using the distribution of the z-score, the program can estimate the number of sequences that would be expected to produce, purely by chance, a z- score greater than or equal to the z-score obtained in the search. This is reported as the E value

18. Search for regions with exact word matches keep 10 highest scoring regions and re-score them using a scoring matrix Join diagonals by introducing gaps Apply Smith-Waterman algorithm to achieve best alignment Calculate Z-score Evaluate significance of Z_scores: E values FASTA :Summary

19. BLAST Basic Local Alignment Search Technique A set of tools developed at NCBI (BlastN, BlastP,..) BLAST benefits –Search speed –Ease of use –Statistical rigor

20. Query sequence Words of length W (1) (2) Compare the word list to the database and identify exact matches BLAST Algorithm W default = 11

21. (3) For each word match, extend alignment in both directions (4) Compute E-value

22. Bit score (S) –Similar to alignment score –Normalized –Higher means more significant E value: Number of hits of score ≥ S expected by chance –Based on random database of similar size –Lower means more significant –Used to assess the statistical significance of the alignment

23. The Gapped Blast algorithm allows several segments that are separated by short gaps to be connected together to one alignment. Gapped BLAST

24. AAAAAAAAAAA ATATATATATATA Alu sequences Low Complexity Sequences